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English(EN) Robust performance metrics for imbalanced classification problems

为不平衡分类问题提出新指标

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文介绍了对不平衡分类问题中常用性能指标的鲁棒性改进。作者们证明，当类别不平衡严重时，像 Matthews 相关系数 (MCC) 和 F1 分数这样的现有指标可能会不公平地偏袒忽略少数类别的分类器。提出的改进包括一个调整参数，以适应针对类别不平衡的鲁棒性，确保少数类别的真正率保持远离零。 AI

影响 为机器学习模型引入了改进的评估指标，特别是在数据集不平衡的情况下。

排序理由 该集群包含一篇详细介绍机器学习模型评估新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

为不平衡分类问题提出新指标

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该集群包含一篇详细介绍机器学习模型评估新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Hajo Holzmann, Bernhard Klar ·

    imbalanced classification problems 的稳健性能指标

    arXiv:2404.07661v2 Announce Type: replace Abstract: We show that established performance metrics in binary classification, such as Matthews' correlation coefficient (MCC), Cohen's $\kappa$, the F-score or the Jaccard similarity coefficient are not robust to class imbalance in the…

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